In addition to using the ratings on each of the 13 music genres, we considered two steps of dimensionality reduction as part of feature engineering. First, to reveal the underlying structure of the 13 genre preferences, we performed an exploratory factor analysis using principal axis factoring with promax rotation. A 5-factor solution was retained, which explained 67% of total data variance: {jazz, classical, latin}, {punk, heavy metal, rap/hip-hop}, {pop, R&B}, {country, christian, folk}, and {rock, alternative pop/rock} (genres ordered by decreasing factor loading). Similar factors have been reported for Greek listeners [1]}.
Second, to quantify the musical diversity of a given respondent, the loadings of each of the 13 music genres onto the five factors were considered as that genre's vector representation in the “preference space” and were used to compute a type of generalist-specialist (GS) score, inspired by the work of Anderson et al. [2]}. Intuitively, generalists versus specialists will have genre vectors spread apart versus close together in the preference space.
We first calculate the user centroid \(\overrightarrow{ct_i}\)  of the genre vectors representing the loadings of the genre on the five factors \(\overrightarrow{l_j}\) , weighted by the number of genre scores rated by each respondent \(w_j\) :
\(\overrightarrow{ct_i} = \frac{1}{\sum {w_j}} \cdot \sum {w_j \overrightarrow{l_j}} \; .\) 
